The literature draws on three dominant modes of theorizing in organizational research. First, a universalistic perspective that comprises direct effects between independent and dependent variables. Second, a contingency perspective, which extends universalistic relationships and suggests interactions between independent variables. Third, a configurational perspective, which aims at identifying unique patterns of factors. For example, a specific pattern of various HR practices or an organization’s strategic type might increase performance beyond what could be explained by direct or interaction effects. However, empirical research has mostly been confined to the first two modes, arguably due to the lack of suitable methods to test configurational predictions. In this article, I present a new approach to test configurations (e.g., ideal types or patterns of factors) that is based on the concept of spatial dependence. I develop a two-step statistical approach that first tests for the existence of ideal types or configurations in the data and then allows the identification of pre-defined types or patterns. The usefulness of this approach is shown in Monte Carlo simulations.